Using AI, genetics and neuroimaging to target the glymphatic system

Using AI, genetics and neuroimaging to target the glymphatic system

The glymphatic system is a brain-wide perivascular clearance pathway which facilitates the clearance of A$\beta$, a pathological hallmark of Alzheimer’s disease, from the brain. The function of the glymphatic system relies highly on the water channel aquaporin 4 (AQP4), a protein which is expressed by the AQP4 gene. Identifying genetic variants of risk with respect to the function of the glymphatic system and the role of sleep on its function, and defining genetically stratified groups to identify suitable candidates for future studies will aid in the investigation of a link between sleep and neurodegeneration. However, the effects of single genetic variants are typically very small. Additionally the interaction between genes or single nucleotide polymorphisms (SNPs), as well as gene-environment interactions might impede the declaration of single genetic variants as statistically significant.

Explainable AI

Machine learning algorithms like decision trees or tree ensembles can learn patterns in data and are therefore promising tools to address the interactions of genes, environment and lifestyle with respect to a certain trait or disease. Explainable AI (XAI) is an emerging area of research in the field of AI. Explainability gives insight into ML models, especially into the decision processes, and might therefore aid in scientific discovery. The explanations can refer to feature importance and feature effect, which indicates the direction and magnitude the feature value has on the predicted outcome, as well as the interaction of features. Different XAI methods might provide complementary information. We use explainable AI on large population-based datasets to study the impact of variations of genes associated with glymphatic clearance on measures of neurodegeneration like amyloid PET.

Interpreting impact of genetic variation using AI models

We then use deep learning models to analyze the regulatory aspects of the genetic variation and to study the impact of variants on gene expression. This helps us to find possible mechanistic interpretations for the interaction of SNPs.